Data filtering

This is the code used to identify and cut out each of the lifts from the IMU’s data. This process has been repeated for each subject, and the resulting data is stored as seperate R dataframe files for further analysis. The process is described below

Lets load all functions from the previous chapter. This is a hassle since they are stored in Quarto markdown language, and R only accept real R code.

# Clean workspace
rm(list = ls())
library(tidyr)
library(dplyr)
library(plotly)
library(ggplot2)
library(knitr)
library(lubridate)

# Most dependencies are loaded by loading datafiltering.qmd.
# To load only the chuncks containing functions we need parsermd
library(parsermd)

toload <- c("load_data","load_plots", "filter_data", "separate_lifts", "visualise_seperate_lifts", "loadXsenseData2")
rmd <- parse_rmd("datafiltering.qmd")


for (i in seq_along(toload)) {
  setup_chunk <- rmd_select(rmd, toload[i]) |> 
    as_document()

  setup_chunk <- setup_chunk[-grep("```", setup_chunk)]
  setup_chunk
#> [1] "library(tidyr)"   "library(stringr)" ""                

  eval(parse(text = setup_chunk))             
}
rm(rmd, i, setup_chunk, toload)

I’ve collected metadata about all recorded lift in liftinfo.csv. This can be used to easialy load the lifts:

metadata <- read.csv("../../Logs/metadata.csv", header = TRUE, sep = ";")

Here is the code to cut each lift and store it as a R object

dir = "../../Logs/new"
files <- list.files(path = dir, full.names = TRUE)

data <- LoadXsenseData2(files[1])
plot_a(data)

data1 <- filterdata(data)
data1 <- separatelifts(data1)

i = 1
plot_filtered_subplots(data1)

Warning: Specifying width/height in layout() is now deprecated.
Please specify in ggplotly() or plot_ly()

This was used to loop throuhg all the lifts

#! A mistake of mine, This filters the analysed data (with the absolute values) instead of the raw imu data. 
cat("lift ", i)
proefpersoon <- "female1_2105"
cat("start time: ")
start_time <- readline()
cat("end time: ")
end_time <- readline()
df <- data1[[i]]
  
time_column <- "time"
output_filename <- paste0(proefpersoon, i, ".csv")
  
# Filter the dataframe
filtered_df <- df[df[[time_column]] >= start_time & df[[time_column]] <= end_time, ]
# Save the filtered dataframe to a CSV file
write.csv(filtered_df, output_filename, row.names = FALSE)

i <- i + 1

The next chapter will describe the analysis of the lifts.